Model identification and characterization of error structures in signal processing
US5784297A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Jan 13, 1997 |
| Grant date | Jul 21, 1998 |
| Priority date | — |
| Expiry date | Jan 13, 2017 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/2321
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
A method for finding a probability density function (PDF) and its statistical moments for an arbitrary exponential function of the form g(x)=.alpha.x.sup.m e.sup.-.beta.x.spsp.n,0<x<.infin., where .alpha., .beta., n>0, m>-1 are real constants in one-dimensional distributions and g(x.sub.1,x.sub.2, . . . ,x.sub.l) in the hyperplane. Non-linear regression analyses are performed on the data distribution and a root-mean-square (RMS) is calculated and recorded for each solution set until convergence. The basis function is reconstructed from the estimates in the final solution set and a PDF is obtained. The moment generating function (MGF), which characterizes any statistical moment of the distribution, is obtained using a novel function derived by the inventors and the mean and variance are obtained in standard fashion. Simple hypotheses about the behavior of such functional forms may be tested statistically once the empirical least squares methods have identified an applicable model derived from actual measurements.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.